arXiv cs.AI· Nhat-Long Nguyen, Viet-Loi Dinh, Trong-Nghia Pham·· 1 天前AI 评分24
KQML-Liver：结合 Kolmogorov-Arnold 网络的可解释混合量子机器学习用于肝病分类
KQML-Liver: Interpretable Hybrid Quantum Machine Learning with Kolmogorov-Arnold Networks for Liver Disease Classification
AI 导读
KQML-Liver 提出一种融合量子机器学习与 Kolmogorov-Arnold 网络（KAN）的混合框架，用于肝病分类。该架构将参数化量子电路与 KAN 函数映射结合，在防止数据泄漏的严格预处理下于印度肝病患者数据集（ILPD）上取得 82% 分类准确率和 100% 召回率，优于现有基线并提升临床预测透明度。
正文 · 原文
Abstract:Liver disease remains a major global health concern, highlighting the need for reliable and accessible early detection methods. Although recent advances in machine learning, deep learning and quantum machine learning (QML) have introduced promising diagnostic tools, many approaches suffer from limited interpretability and inflated performance caused by data leakage or overly simplistic evaluation protocols. These constraints reduce their reliability in real-world clinical practice. This paper proposes KQML-Liver, a hybrid framework that integrates QML with Kolmogorov-Arnold Networks (KANs) to enhance predictive performance and interpretability. The architecture combines parameterized quantum circuits for expressive feature representation with KAN-based functional mappings that capture nonlinear relationships in an interpretable manner. Evaluated on the Indian Liver Patient Dataset (ILPD) under a rigorous preprocessing protocol preventing data leakage, the proposed model achieves 82% classification accuracy and 100% recall, detecting all positive cases, outperforming existing baselines while providing improved transparency in clinical predictions. These results suggest that hybrid KAN-quantum architectures offer a promising direction for interpretable AI-assisted medical diagnosis.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15241 [cs.LG] |
| (or arXiv:2611.15241v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15241 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Long Nguyen Nhat [view email]
[v1]
Wed, 11 Nov 2026 09:31:35 UTC (3,487 KB)
来源：arXiv cs.AI · arxiv.org